V. H. Quintana

Bio: V. H. Quintana is an academic researcher from University of Waterloo. The author has contributed to research in topics: Estimator & AC power. The author has an hindex of 4, co-authored 5 publications receiving 168 citations.

Network parameter estimation using online data with application to transformer tap position estimation

Abstract: This paper deals with the estimation of parameters involved in the classical steady-state modelling of network elements within the context of power system state estimation. A general method is proposed for the identification and correction of the real-time data, based on the sensitivity relationship between the measurement residuals and the parameter error; this relationship is treated as an estimation problem, yielding a parameter error estimate. Special emphasis is put on the real-time correction of erroneous transformer tap positions, the most frequent parameter errors during the real-time operation of state estimators. The proposed method is very simple and efficient, since it merely requires a single substitution in the state estimation linear system. Relationships with other methods are also discussed. Simulation results on a real-life system are reported.

Real-time processing of transformer tap positions

Abstract: Reviews the methods for network parameter estimation and correction in power system state estimation and proposes a sequential-type method for real-time processing of transformer tap positions. The method exploits the information contained in the measurement residuals to estimate a better tap position; it uses a linearized sensitivity model to relate the measurement residuals to the tap position error. Based on part of the Belgium HV system, several testing results are reported.

Power system network parameter estimation

Abstract: Methods for power network parameter estimation and correction are discussed in this paper. A sequential-type method for real-time processing of network parameters is proposed. The method exploits the information contained in the measurement residuals to estimate a better network parameter; it uses a linearized sensitivity model to relate the measurement residuals to the parameter error. The proposed method has been coded in FORTRAN using several short-cuts and has been thoroughly tested in the estimation of transformer ratios in the Belgian HV system.

Numerically-robust techniques for the monitoring of power system operations

Abstract: Presents a brief survey of the major advances on numerical techniques to solve the power system state estimation (PSSE) problem. A brief review of the conventional batch estimator based on the solution of the normal equation is presented, together with a discussion on its performance from a numerical point of view. Two numerically robust techniques are described for the solution of the PSSE problem: Golub's method for the solution of the weighted least squares (WLS) problem of a PSSE batch processor, using the Householder orthogonal transformations; and Givens method for the sequential WLS state estimators, using orthogonal transformations performed by rows. A discussion on the sparsity and ordering techniques is included. Finally, a comparison of the two orthogonal techniques with the conventional method, from a computer execution time and storage requirement point of view, is also presented. Several numerical examples are used for such a comparison.

Node decomposition techniques for multiprocessing power system studies

Abstract: Presents a novel method based on node tearing analysis for the solution of power system load flow problems. The technique is applied to the linearized real and reactive power models obtained in the fast decoupled load flow method. By considering node tearing as the reduction of subnetworks to multiterminal equivalents, sparsity and sparse network reduction techniques are widely used. Heuristic structural decomposition techniques are presented. The results of applying this methodology to standard test systems are illustrated and compared with a one-piece solution.

Linear reactive power optimization in a large power network using the decomposition approach

Abstract: A mathematical framework is presented for the solution of the economic dispatch problem The application of the Dantzig-Wolfe decomposition method for the solution of this problem is emphasized The system's optimization problem is decomposed into several subproblems corresponding to specific areas in the power system The upper bound technique along with the decomposition method are applied to a 16-bus system and a modified IEEE 30-bus system, and numerical results are presented for larger systems The results indicate that the presented formulation of the reactive power optimization and the application of the decomposition procedure will facilitate the solution of the problem The algorithm can be applied to a large-scale power network, where its solution represents a significant reduction in the number of iterations and the required computation time >

Generalized state estimation

Abstract: Power system state estimation derives a real-time network model by extracting information from a redundant data set consisting of telemetered, predicted and static data items. This paper describes a generalized, fully developed, estimation approach that fundamentally improves the information extraction process. Its main contribution is the successful inclusion of topology and parameters in the estimation and bad data analysis processes. This is valuable both in the initial commissioning of a state estimator, and in its routine real-time and study mode application. The approach involves a variety of novel concepts and methods. It is usable in weighted least squares (WLS) and other estimation approaches.

Power system parameter estimation: a survey

Abstract: This paper deals with the problem of network parameter errors in state estimation. First of all, some experimental results are presented showing the influence of these errors on the performance of weighted least squares state estimators. Secondly, the preliminary step of identifying suspicious network parameters is briefly discussed. A classification of the techniques proposed in the literature to estimate parameter errors is then suggested, followed by a description of the main ideas behind each method. Finally, a discussion is included on the possibilities and limitations of every class of methods.

Estimation of parameter errors from measurement residuals in state estimation (power systems)

Abstract: Any error of network parameters affects the value of the measurement residuals calculated in state estimation. Explicit mathematical expressions relating the residuals to the parameter errors are derived. A two-step approach is proposed for parameter error estimation. The first step is to estimate a bias vector which combines the effects of parameter errors and the state of the system. A least-square approach using the measurement residuals calculated in each state estimation run is proposed for the first step. After several state estimation runs, a sequence of such bias vectors is obtained. The second step is to estimate the parameter errors from the sequence of bias vectors. A recursive least-square estimation method is proposed for this step. Theoretical and computational issues of the proposed method are addressed. Test results are presented. >

Identification of network parameter errors

Abstract: This paper describes a simple yet effective method for identifying incorrect parameters associated with the power network model. The proposed method has the desired property of distinguishing between bad analog measurements and incorrect network parameters, even when they appear simultaneously. This is accomplished without expanding the state or the measurement vectors. There is also no need to a priori specify a suspect parameter set. All these features are verified via simulations that are carried out using different-size test systems for various possible cases. Implementation of the method involves minor changes in the weighted least-squares state estimation code; hence, it can be easily integrated into existing state estimators as an added feature.